System and method for providing a model usage report for simulation models

ABSTRACT

A system and method for viewing models and model variables within a sophisticated modeling environment is disclosed. The system provides varying levels of insight into a modeling infrastructure to help the user understand model and model variable dependencies, usage, distribution, and/or the like. The method includes storing model and model variable data within a relational database system, receiving a request from a user interfacing with the system via a web interface, extracting search criteria and presentation preferences from the request, formulating and executing one or more queries on the database to retrieve the required data, formatting the data in accordance with the request, and retuning the data to the requesting user in the form of a web page.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims priority to, U.S. Ser.No. 11/564,341 filed on Nov. 29, 2006 and entitled “SYSTEM AND METHODFOR MANAGING SIMULATION MODELS”, the entire contents of which is herebyincorporated by reference.

FIELD OF THE INVENTION

The invention generally relates to the management of models, and moreparticularly, to a system and method for providing empowerment tools,data visualization tools, and impact analysis tools in order to producereports providing greater insight into model interdependencies, modelusage, model distribution, variable usage, model metadata, and systemperformance.

BACKGROUND OF THE INVENTION

A key characteristic that may be found in every free economy iscompetition, and therein lies the importance of an effective marketingstrategy. Marketing takes many forms and has evolved over the years interms of form and delivery in direct response to competition and fluidconsumer behaviors. Thus, corporations expend great effort andexpenditure in developing and modifying effective marketing strategiesin light of perpetual changes occurring within the realms ofcompetition, economy, technological advance, political climate, consumerbehavior, and/or the like. These are but a few of the variablesinfluencing the success or failure of a marketing strategy.

Marketing is an expensive proposition; however, without it, a businessor corporation has little hope of survival, especially in an era ofgrowing consumer options. Ensuring that marketing budgets are directedtoward activities that will provide the largest return at the lowestcost is a vital exercise among corporations. Countless books and guideshave been published over the years by authors who believe that theypossess the magic bullet in marketing strategy. However, it is widelyunderstood that what is effective strategy today, may be a marketingbust tomorrow. Therefore, marketers have adopted various methods toattempt to gauge and forecast the commerce climate, demographic shifts,and behavioral trends.

Computing technologies have increased the marketer's ability to trackmany marketing related variables and to construct models in order tosimulate the outcome of various marketing strategies or campaigns. Inaddition to the countless proprietary software tools that have beendeveloped internally, a number of software companies have produced anumber of “off-the-shelf” solutions that enable a marketer to betterdevelop a sound marketing strategy. These tools are generally built onrecognized strategic marketing practices that have been adapted to thecurrent business environment and the tools accept various inputs inorder to model marketing scenarios.

Regardless of the modeling tool, the complexities of managing vastlibraries of models and model variables can hinder the utilization ofmodels and stretch computing resources. In an environment where multipleusers develop models, a transparency is lacking which enables marketersto view the penetration, utilization, and distribution of models. Thisproblem leads to less than efficient and less than effective use ofmodels. Moreover, because models often include interdependencies withother models, clearing out unused, redundant, or outdated models becomesdifficult. If a model that appears to not have been used for a period oftime is deleted, it may affect other models that were dependent on thedeleted model. Thus, there is a need for a system and method formanaging models in a transparent environment, wherein marketers may viewutilization statistics for models and variables. Further, there is aneed for tools to empower marketers to build dynamic queries andgenerate customized reports on customer behavior and performance. Theseempowerment tools should include data utilization tools to enable themarketer to zoom in and zoom out of complex model hierarchies andrelationships.

SUMMARY OF THE INVENTION

The invention includes a system and method for providing analyticinformation concerning models and the distribution of model variables toprovide a holistic understanding into model relationships anddependencies. The system includes empowerment tools intended tocultivate, share and leverage knowledge, ideas and best practices,accelerate time to develop and deploy models, reduce time to executecampaigns, analyze model usage and trends, monitor data quality andsystem performance, and diagnostics for these items.

Through a number of interfaces, the invention encompasses model andmodel variable analysis within three primary groups; model insight,model analysis, and model reports. The system accepts inputs from a userin the form of model and/or variable search or selection criteria,retrieves information relating to models corresponding to the searchcriteria, and provides a visual representation of model and/or variableattributes within tables or graphs. The user may interact with thesystem via a web interface to view model dependencies, metadata reports,model analysis summary reports, model analysis detailed reports, modelanalysis chart reports, model analysis graph report, variable usage bybusiness unit, variable count reports, variable usage by model type,variable metadata reports, model usage, penetration of models incampaigns, types of decision sciences used in customer marketing, humanresource allocation, and system performance reports in tabular and graphform.

The system maintains up-to-date information relating to models and modelvariables within a number of database tables. These tables maintaininformation relating to models, model summaries, model owners, modeldependencies, variables, variable classes, variable types, and/or thelike. The system provides a utility to query the various tables inresponse to a request from a user, format query results according touser preferences or parameters, and provide the data to the requestinguser within a web page.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the invention may be derived byreferring to the detailed description and claims when considered inconnection with the Figures, wherein like reference numbers refer tosimilar elements throughout the Figures, and:

FIG. 1 is a block diagram illustrating the major system components forexemplary tools for providing knowledge and insight into a complexmodeling environment, according to an embodiment of the presentinvention;

FIG. 2 is a data diagram showing tables and table relationships forexemplary tools for providing knowledge and insight into a complexmodeling environment, according to an embodiment of the presentinvention;

FIG. 3 is a screenshot of an exemplary interface for displaying modelrelationships and model variables, according to an embodiment of thepresent invention;

FIG. 4 is a screenshot of an exemplary interface for displaying modelmetadata and variable values, according to an embodiment of the presentinvention;

FIG. 5 is a screenshot of an exemplary interface for entering modelsearch criteria to retrieve a listing of corresponding models, accordingto an embodiment of the present invention;

FIG. 6 is a screenshot of an exemplary interface for displaying modelsdirectly and indirectly impacted by a selected model, according to anembodiment of the present invention;

FIG. 7 is a screenshot of an exemplary interface for displaying detailedmodel attributes, according to an embodiment of the present invention;

FIG. 8 is a screenshot of an exemplary interface for displaying a chartshowing the distribution of selected models, according to an embodimentof the present invention;

FIG. 9 is a screenshot of an exemplary interface for displaying modelvariable usage according to business unit, according to an embodiment ofthe present invention;

FIG. 10 is a screenshot of an exemplary interface for displaying adetailed variable count report, according to an embodiment of thepresent invention;

FIG. 11 is a screenshot of an exemplary interface for displaying avariable metadata report, according to an embodiment of the presentinvention;

FIG. 12 is a screenshot of an exemplary interface for displaying asystem performance report in tabular form, according to an embodiment ofthe present invention; and,

FIG. 13 is a screenshot of an exemplary interface for displaying asystem performance report in chart form, according to an embodiment ofthe present invention;

FIG. 14 is a screenshot of an exemplary interface for displaying a humanresource allocation report in chart form, according to an embodiment ofthe present invention;

FIG. 15 is a screenshot of an exemplary interface for displaying a modelusage frequency report, according to an embodiment of the presentinvention; and,

FIG. 16 is a screenshot of an exemplary interface for displaying modelusage according to penetration and decision sciences, according to anembodiment of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The detailed description of exemplary embodiments of the inventionherein makes reference to the accompanying drawings, which show theexemplary embodiment by way of illustration and its best mode. Whilethese exemplary embodiments are described in sufficient detail to enablethose skilled in the art to practice the invention, it should beunderstood that other embodiments may be realized and that logical andmechanical changes may be made without departing from the spirit andscope of the invention. Thus, the detailed description herein ispresented for purposes of illustration only and not of limitation.

For the sake of brevity, conventional data networking, applicationdevelopment and other functional aspects of the systems (and componentsof the individual operating components of the systems) may not bedescribed in detail herein. Furthermore, the connecting lines shown inthe various figures contained herein are intended to represent exemplaryfunctional relationships and/or physical couplings between the variouselements. It should be noted that many alternative or additionalfunctional relationships or physical connections may be present in apractical system.

In general, the invention includes a system and method for performinganalysis on models and variables within a complex modeling environment.As used herein, “model” or similar terms may include any historic,current or probability data encompassing elements of campaigns,promotions, affiliates, joint promotions, rejections, click-thru,demographics, special offers, earning of loyalty points, redemption ofloyalty points, consumer spend, special events, and/or the like. Modelsand model variables provide a degree of certainty regarding the outcomeor effectiveness of campaign scenarios based on, for example, the abovementioned historic data.

With reference to FIG. 1, system 100 facilitates interaction between auser 105 and the Model Vision System (MVS) 175 through, in oneembodiment, a web client 110 with a network connection to an intranetserver 120. Intranet server 120 may employ an authentication server 125in order to validate and assign proper permissions to authorized usersof MVS 175. User database 130 stores user credentials and permissionsspecific to each user. Intranet server 120 also employs an applicationsserver 135 to manage various applications and utilities that areutilized by the system. Application server 135 may be a stand-aloneserver or may comprise software residing within intranet server 120. Inone embodiment, Model Vision Utility (MVU) 145 is invoked by applicationserver 135 to query model database 150, retrieve data via datamanagement server 140, and perform complex calculations and dataformatting for presentation to user 105 and/or any other designatedthird-party. Model database 150 maintains data pertaining to models andmodel variables. Application server 135 also interfaces with a reportengine 155 to create pre-configured and/or ad-hoc reports as will bediscussed in greater detail herein.

In addition to the components described above, MVS 175 may furtherinclude one or more of the following: a host server or other computingsystems including a processor for processing digital data; a memorycoupled to the processor for storing digital data; an input digitizercoupled to the processor for inputting digital data; an applicationprogram stored in the memory and accessible by the processor fordirecting processing of digital data by the processor; a display devicecoupled to the processor and memory for displaying information derivedfrom digital data processed by the processor; and a plurality ofdatabases. Various databases used herein may include: user database 130,model database 150; internal data 160; external data 165; campaign data170 and/or like data useful in the operation of system 100.

MVS 178 may connect with any number of external systems and databases toacquire data relevant to the modeling environment. Internal data 160,external data 165, and campaign data 170 may be acquired via datamanagement server 140 for use in developing models, business plans, andmarketing strategies. Specifically, MVS 178 may use this data inconjunction with the various tools and reports disclosed hereinincluding, for example, analysis of data anomalies, businesspenetration, model distribution, model usage, variable usage, systemperformance, human resource allocation, and penetration of decisionsciences.

As will be appreciated by one of ordinary skill in the art, one or moreof the components of system 100 may be embodied as a customization of anexisting system, an add-on product, upgraded software, a stand alonesystem (e.g., kiosk), a distributed system, a method, a data processingsystem, a device for data processing, and/or a computer program product.Accordingly, individual system 100 components may take the form of anentirely software embodiment, an entirely hardware embodiment, or anembodiment combining aspects of both software and hardware. Furthermore,individual system 100 components may take the form of a computer programproduct on a computer-readable storage medium having computer-readableprogram code means embodied in the storage medium. Any suitablecomputer-readable storage medium may be utilized, including hard disks,CD-ROM, optical storage devices, magnetic storage devices, and/or thelike.

The invention contemplates uses in association with web services,utility computing, pervasive and individualized computing, security andidentity solutions, autonomic computing, commodity computing, mobilityand wireless solutions, open source, biometrics, grid computing and/ormesh computing.

User 105 may include any individual, business, entity, governmentorganization, software and/or hardware that interact with system 100 toview and analyze model relationships and interdependencies, variableinterdependencies, model use statistics, and/or the like. User 105 maybe, for example, a program manager who interacts with system 100 todetermine how models are being utilized by her department for campaigns.User 105 may interface with intranet server 120 via any communicationprotocol, device or method discussed herein or known in the art. In oneembodiment, user 100 may interact with MVS 175 via an Internet browserat a web client 110.

Web client 110 comprises any hardware and/or software suitablyconfigured to facilitate input, receipt and/or review of informationrelating to models and variables or any information discussed herein.Web client 110 includes any device (e.g., personal computer) whichcommunicates (in any manner discussed herein) with MVS 175 via anynetwork discussed herein. Such browser applications comprise Internetbrowsing software installed within a computing unit or system to conductonline transactions and/or communications. These computing units orsystems may take the form of a computer or set of computers, althoughother types of computing units or systems may be used, includinglaptops, notebooks, hand held computers, set-top boxes, workstations,computer-servers, main frame computers, mini-computers, PC servers,pervasive computers, network sets of computers, and/or the like.Practitioners will appreciate that web client 110 may or may not be indirect contact with MVS 175. For example, web client 110 may access theservices of MVS 175 through another server, which may have a direct orindirect connection to intranet server 120.

As those skilled in the art will appreciate, web client 110 includes anoperating system (e.g., Windows NT, 95/98/2000, OS2, UNIX, Linux,Solaris, MacOS, etc.) as well as various conventional support softwareand drivers typically associated with computers. Web client 110 mayinclude any suitable personal computer, network computer, workstation,minicomputer, mainframe or the like. Web client 110 can be in a home orbusiness environment with access to a network. In an exemplaryembodiment, access is through a network or the Internet through acommercially available web-browser software package.

Web client 110 may be independently, separately or collectively suitablycoupled to the network via data links which includes, for example, aconnection to an Internet Service Provider (ISP) over the local loop asis typically used in connection with standard modem communication, cablemodem, Dish networks, ISDN, Digital Subscriber Line (DSL), or variouswireless communication methods, see, e.g., GILBERT HELD, UNDERSTANDINGDATA COMMUNICATIONS (1996), which is hereby incorporated by reference.It is noted that the network may be implemented as other types ofnetworks, such as an interactive television (ITV) network.

Firewall 115, as used herein, may comprise any hardware and/or softwaresuitably configured to protect MVS 175 components from users of othernetworks. Firewall 115 may reside in varying configurations includingstateful inspection, proxy based and packet filtering among others.Firewall 115 may be integrated as software within intranet server 120,any other system components or may reside within another computingdevice or may take the form of a standalone hardware component.

Intranet server 120 may include any hardware and/or software suitablyconfigured to facilitate communications between web client 110 and oneor more MVS 175 components. Further, intranet server 120 may beconfigured to transmit data to web client 110 within markup languagedocuments. As used herein, “data” may include encompassing informationsuch as commands, queries, files, data for storage, and/or the like indigital or any other form. Intranet server 120 may operate as a singleentity in a single geographic location or as separate computingcomponents located together or in separate geographic locations.

Intranet server 120 may provide a suitable web site or otherInternet-based graphical user interface which is accessible by users. Inone embodiment, the Microsoft Internet Information Server (IIS),Microsoft Transaction Server (MTS), and Microsoft SQL Server, are usedin conjunction with the Microsoft operating system, Microsoft NT webserver software, a Microsoft SQL Server database system, and a MicrosoftCommerce Server. Additionally, components such as Access or MicrosoftSQL Server, Oracle, Sybase, Informix MySQL, InterBase, etc., may be usedto provide an Active Data Object (ADO) compliant database managementsystem.

Any of the communications, inputs, storage, databases or displaysdiscussed herein may be facilitated through a web site having web pages.The term “web page” as it is used herein is not meant to limit the typeof documents and applications that might be used to interact with theuser. For example, a typical web site might include, in addition tostandard HTML documents, various forms, Java applets, JavaScript, activeserver pages (ASP), common gateway interface scripts (CGI), extensiblemarkup language (XML), dynamic HTML, cascading style sheets (CSS),helper applications, plug-ins, and/or the like. A server may include aweb service that receives a request from a web server, the requestincluding a URL (http://yahoo.com/stockquotes/ge) and an IP address(123.56.789). The web server retrieves the appropriate web pages andsends the data or applications for the web pages to the IP address. Webservices are applications that are capable of interacting with otherapplications over a communications means, such as the Internet. Webservices are typically based on standards or protocols such as XML,SOAP, WSDL and UDDI. Web services methods are well known in the art, andare covered in many standard texts. See, e.g., ALEX NGHIEM, IT WEBSERVICES: A ROADMAP FOR THE ENTERPRISE (2003), hereby incorporated byreference.

Application server 135 may include any hardware and/or software suitablyconfigured to serve applications and/or data to a connected web client110. Like intranet server 120, application server 135 may communicatewith any number of other servers, databases and/or components throughany means known in the art. Further, application server 135 may serve asa conduit between web client 110 and the various systems and componentsof the MVS 175. Intranet server 120 may interface with applicationserver 135 through any means known in the art including a LAN/WAN, forexample. Application server 135 may further invoke MVU 145, datamanagement server 140, and/or report engine 165 in response to user 105requests.

MVU 145 may include any hardware and/or software suitably configured toreceive requests from web client 110 via intranet server 120 and/orapplication server 135. MVU 145 is further configured to processrequests, construct database queries, and execute queries against modeldatabase 150. MVU 145 receives data from model database 150, formats thedata, and passes the data to intranet server 120 via application server135. Application server 135 constructs a markup language document basedon the data and transmits the document to web client 110 for displaywithin a browser application. In one embodiment, MVU 145 may beconfigured to interact with other MVS 175 components to perform complexcalculations, retrieve additional data, format data into reports, createXML representations of data, construct markup language documents, and/orthe like. Moreover, MVU 145 may reside as a standalone system or may beincorporated with application server or any other MVS 175 component asprogram code.

Data management server 140 may include any hardware and/or softwaresuitably configured to facilitate communications between MVU 145 and oneor more data sources. Specifically, the data management server 140 mayinclude a middleware product to facilitate communication with varyingtypes of databases residing on disparate hosts. Data may be collectedfrom an internal source 160, and external source 165, as well as dataspecific to one or more marketing campaigns 170. This data may serve asinputs to the modeling process to determine the probable outcome ofmarketing activity. Moreover, MVU 145 may utilize this data indetermining the effectiveness of models or in diagnostics to determineif model output is consistent with results from real-world campaign.

Report engine 155 may include any hardware and/or software suitablyconfigured to produce reports from information stored in one or moredatabases. Report engines are commercially available and known in theart. Report engine 155 may provide printed reports, web access toreports, graphs, real-time information, raw data, batch informationand/or the like. Report engine 155 may be implemented throughcommercially available hardware and/or software, through custom hardwareand/or software components, or through a combination thereof. Further,report engine 155 may reside as a standalone system within MVS 175 or asa component of application server 135 or intranet server 120.

In order to control access to application server 135 or any othercomponent of MVS 175, intranet server 120 may invoke an authenticationserver 125 in response to user 105 submissions of authenticationcredentials received at intranet server 120. Authentication server 125may include any hardware and/or software suitably configured to receiveauthentication credentials, encrypt and decrypt credentials,authenticate credentials, and grant access rights according topre-defined privileges attached to the credentials. Authenticationserver 125 may grant varying degrees of application and data levelaccess to users based on information stored within user database 130.For example, a system administrator may be granted access to inputand/or modify models and variables, while a marketing manager may belimited to model and variable analysis only.

User database 130 may include any hardware and/or software suitablyconfigured to facilitate storing identification, authenticationcredentials, and user permissions. Model database 150 stores datarelating models and model variables. One skilled in the art willappreciate that system 100 may employ any number of databases in anynumber of configurations. Further, any databases discussed herein may beany type of database, such as relational, hierarchical, graphical,object-oriented, and/or other database configurations. Common databaseproducts that may be used to implement the databases include DB2 by IBM(White Plains, N.Y.), various database products available from OracleCorporation (Redwood Shores, Calif.), Microsoft Access or Microsoft SQLServer by Microsoft Corporation (Redmond, Wash.), or any other suitabledatabase product. Moreover, the databases may be organized in anysuitable manner, for example, as data tables or lookup tables. Eachrecord may be a single file, a series of files, a linked series of datafields or any other data structure. Association of certain data may beaccomplished through any desired data association technique such asthose known or practiced in the art. For example, the association may beaccomplished either manually or automatically. Automatic associationtechniques may include, for example, a database search, a databasemerge, GREP, AGREP, SQL, using a key field in the tables to speedsearches, sequential searches through all the tables and files, sortingrecords in the file according to a known order to simplify lookup,and/or the like. The association step may be accomplished by a databasemerge function, for example, using a “key field” in pre-selecteddatabases or data sectors.

More particularly, a “key field” partitions the database according tothe high-level class of objects defined by the key field. For example,certain types of data may be designated as a key field in a plurality ofrelated data tables and the data tables may then be linked on the basisof the type of data in the key field. The data corresponding to the keyfield in each of the linked data tables is preferably the same or of thesame type. However, data tables having similar, though not identical,data in the key fields may also be linked by using AGREP, for example.In accordance with one aspect of system 100, any suitable data storagetechnique may be utilized to store data without a standard format. Datasets may be stored using any suitable technique, including, for example,storing individual files using an ISO/IEC 7816-4 file structure;implementing a domain whereby a dedicated file is selected that exposesone or more elementary files containing one or more data sets; usingdata sets stored in individual files using a hierarchical filing system;data sets stored as records in a single file (including compression, SQLaccessible, hashed via one or more keys, numeric, alphabetical by firsttuple, etc.); Binary Large Object (BLOB); stored as ungrouped dataelements encoded using ISO/IEC 7816-6 data elements; stored as ungroupeddata elements encoded using ISO/IEC Abstract Syntax Notation (ASN.1) asin ISO/IEC 8824 and 8825; and/or other proprietary techniques that mayinclude fractal compression methods, image compression methods, etc.

In one exemplary embodiment, the ability to store a wide variety ofinformation in different formats is facilitated by storing theinformation as a BLOB. Thus, any binary information can be stored in astorage space associated with a data set. As discussed above, the binaryinformation may be stored on the financial transaction instrument orexternal to but affiliated with the financial transaction instrument.The BLOB method may store data sets as ungrouped data elements formattedas a block of binary via a fixed memory offset using either fixedstorage allocation, circular queue techniques, or best practices withrespect to memory management (e.g., paged memory, least recently used,etc.). By using BLOB methods, the ability to store various data setsthat have different formats facilitates the storage of data associatedwith system 90 by multiple and unrelated owners of the data sets. Forexample, a first data set which may be stored may be provided by a firstparty, a second data set which may be stored may be provided by anunrelated second party, and yet a third data set which may be stored,may be provided by an third party unrelated to the first and secondparty. Each of these three exemplary data sets may contain differentinformation that is stored using different data storage formats and/ortechniques. Further, each data set may contain subsets of data that alsomay be distinct from other subsets.

As stated above, in various embodiments of system 90, the data can bestored without regard to a common format. However, in one exemplaryembodiment of the invention, the data set (e.g., BLOB) may be annotatedin a standard manner when provided for manipulating the data onto thefinancial transaction instrument. The annotation may comprise a shortheader, trailer, or other appropriate indicator related to each data setthat is configured to convey information useful in managing the variousdata sets. For example, the annotation may be called a “conditionheader”, “header”, “trailer”, or “status”, herein, and may comprise anindication of the status of the data set or may include an identifiercorrelated to a specific issuer or owner of the data. In one example,the first three bytes of each data set BLOB may be configured orconfigurable to indicate the status of that particular data set; e.g.,LOADED, INITIALIZED, READY, BLOCKED, REMOVABLE, or DELETED. Subsequentbytes of data may be used to indicate for example, the identity of theissuer, user, transaction/membership account identifier or the like.Each of these condition annotations are further discussed herein.

The data set annotation may also be used for other types of statusinformation as well as various other purposes. For example, the data setannotation may include security information establishing access levels.The access levels may, for example, be configured to permit only certainindividuals, levels of employees, companies, or other entities to accessdata sets, or to permit access to specific data sets based on thetransaction, merchant, issuer, user or the like. Furthermore, thesecurity information may restrict/permit only certain actions such asaccessing, modifying, and/or deleting data sets. In one example, thedata set annotation indicates that only the data set owner or the userare permitted to delete a data set, various identified users may bepermitted to access the data set for reading, and others are altogetherexcluded from accessing the data set. However, other access restrictionparameters may also be used allowing various entities to access a dataset with various permission levels as appropriate.

The data, including the header or trailer may be received by astand-alone interaction device configured to add, delete, modify, oraugment the data in accordance with the header or trailer. As such, inone embodiment, the header or trailer is not stored on the transactiondevice along with the associated issuer-owned data but instead theappropriate action may be taken by providing to the transactioninstrument user at the stand-alone device, the appropriate option forthe action to be taken. System 100 contemplates a data storagearrangement wherein the header or trailer, or header or trailer history,of the data is stored on the transaction instrument in relation to theappropriate data.

One skilled in the art will also appreciate that, for security reasons,any databases, systems, devices, servers or other components of system100 may consist of any combination thereof at a single location or atmultiple locations, wherein each database or system includes any ofvarious suitable security features, such as firewalls, access codes,encryption, decryption, compression, decompression, and/or the like.

The invention may be described herein in terms of functional blockcomponents, screen shots, optional selections and various processingsteps. It should be appreciated that such functional blocks may berealized by any number of hardware and/or software components configuredto perform the specified functions. For example, system 100 may employvarious integrated circuit components, e.g., memory elements, processingelements, logic elements, look-up tables, and/or the like, which maycarry out a variety of functions under the control of one or moremicroprocessors or other control devices. Similarly, the softwareelements of system 90 may be implemented with any programming orscripting language such as C, C++, Java, COBOL, assembler, PERL, VisualBasic, SQL Stored Procedures, extensible markup language (XML), with thevarious algorithms being implemented with any combination of datastructures, objects, processes, routines or other programming elements.Further, it should be noted that system 90 may employ any number ofconventional techniques for data transmission, signaling, dataprocessing, network control, and/or the like. Still further, system 90could be used to detect or prevent security issues with a client-sidescripting language, such as JavaScript, VBScript or the like. For abasic introduction of cryptography and network security, see any of thefollowing references: (1) “Applied Cryptography: Protocols, Algorithms,And Source Code In C,” by Bruce Schneier, published by John Wiley & Sons(second edition, 1995); (2) “Java Cryptography” by Jonathan Knudson,published by O'Reilly & Associates (1998); (3) “Cryptography & NetworkSecurity: Principles & Practice” by William Stallings, published byPrentice Hall; all of which are hereby incorporated by reference.

These software elements may be loaded onto a general purpose computer,special purpose computer, or other programmable data processingapparatus to produce a machine, such that the instructions that executeon the computer or other programmable data processing apparatus createmeans for implementing the functions specified in the flowchart block orblocks. These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function specified in the flowchart block or blocks.The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchartillustrations support combinations of means for performing the specifiedfunctions, combinations of steps for performing the specified functions,and program instruction means for performing the specified functions. Itwill also be understood that each functional block of the block diagramsand flowchart illustrations, and combinations of functional blocks inthe block diagrams and flowchart illustrations, can be implemented byeither special purpose hardware-based computer systems which perform thespecified functions or steps, or suitable combinations of specialpurpose hardware and computer instructions. Further, illustrations ofthe process flows and the descriptions thereof may make reference touser windows, web pages, web sites, web forms, prompts, etc.Practitioners will appreciate that the illustrated steps describedherein may comprise in any number of configurations including the use ofwindows, web pages, web forms, popup windows, prompts and/or the like.It should be further appreciated that the multiple steps as illustratedand described may be combined into single web pages and/or windows buthave been expanded for the sake of simplicity. In other cases, stepsillustrated and described as single process steps may be separated intomultiple web pages and/or windows but have been combined for simplicity.

Practitioners will appreciate that there are a number of methods fordisplaying data within a browser-based document. Data may be representedas standard text or within a fixed list, scrollable list, drop-downlist, editable text field, fixed text field, pop-up window, and/or thelike. Likewise, there are a number of methods available for modifyingdata in a web page such as, for example, free text entry using akeyboard, selection of menu items, check boxes, option boxes, and/or thelike.

Referring to FIG. 2, an exemplary data diagram is shown for managingmodels and model variables within a complex modeling architecture. Inone embodiment, the data diagram includes Continue₁₃Variables 200.Continue₁₃Variables 205, Variables 210, Variable₁₃Class 215,Variable₁₃Type 220, Model₁₃Dependency 225, Model 230, Dataset_Col 235,Source_File 240, Model₁₃ Owner 245, Model_Performance 250, Model_Sum 255and Dataset 260. The tables, fields, key fields, and table relationshipsare presented for explanation only and are not intended to limit thescope of the invention. Practitioners will appreciate that a relationaldatabase may be arranged in any number of ways without significantlyaltering the storage, modification, retrieval, and deletion of data fromthe perspective of a front-end computer application. In other words, anyof the illustrated tables may be divided into two or more tables.Likewise any two or more tables may be combined into one table.

The screenshots of FIGS. 3-13 are merely embodiments of interfaces tosystem 100 and are not intended to limit the scope of the invention asdescribed herein. For example, the steps recited in any of the method orprocess descriptions may be executed in any order and are not limited tothe order presented. It will be appreciated that the followingdescription makes appropriate references not only to the elementsdepicted in FIGS. 3-13, but also to the various system components asdescribed above with reference to FIG. 1 and the data diagram of FIG. 2.

As disclosed herein, system 100 provides users with a number of viewsinto a model environment to assist in the design, management,utilization, and distribution of models and model variables. In oneembodiment, the various views are categorized into three primaryknowledge areas; Model Insight, Model Analysis, and Model Reports. Thus,user 105 accessing system 100 via web client 110 is first presented aModel Vision home page that may include any number of links and otherinformation. The Model Vision home page specifically includes a menuwith links to each of the primary knowledge areas. Furthermore, theModel Vision home page may include direct links to each view within eachof the three primary knowledge areas.

FIG. 3 is a screenshot of an exemplary interface for displaying modelrelationships and model variables in accordance with an embodiment ofthe present invention. When an authorized user connects with MVS 175 viaweb client 110, the user is presented with the Model Vision home pagethat includes links to three primary model knowledge areas.Practitioners will appreciate that the links may comprise hyperlinks,dropdown menus, check boxes, and/or the like. From the Model Vision homepage, user 105 may select a link to access a Model Insight home page.From the Model Insight home page, user 105 is presented with options toview model dependencies or model metadata. Both model dependency andmodel metadata options may comprise dropdown menus that arepre-populated with a list of model identifiers retrieved from the Modeltable 230. On selecting a model identifier in the model dependencydropdown menu, the model identifier is transmitted from web client 110to intranet server 120 via a request. Intranet server 120 verifies thatthe request is from an active session, or whether authenticationcredentials are required of the user. Practitioners will appreciate thatsystem 100 may use any known method for maintaining state in a webenvironment, including through the use of cookies, hidden form fields,Active Server pages (ASP), Java Server pages (JSP), and/or the like.

Intranet server 120 passes the request to application server 135, whichinvokes MVU 145 to format the request, construct a SQL query, establisha connection with model database 150, pass the SQL query to modeldatabase 150, receive query results, format the results, and pass theformatted results to intranet server 120 via application server 135.Intranet server 120 compiles the results into a hypertext languagedocument and transmits the document to web client 110 where it isdisplayed as a web page within a web browser. Furthermore, MVU 145 orapplication server 135 may invoke report engine 155 to format resultsbefore passing the results to internet server 120.

The Model Dependency web page 300 includes a chart that graphicallyrepresents the selected model as the parent model in a dependency tree305. Branching from the parent model 310 are all models that aredirectly or indirectly dependent on the parent 310. As the ModelDependency web page 300 is loaded at web client 110, MVU 145 queriesmodel database 150 based on the parent model identifier and retrievesall records from Model_Dependency table 225 corresponding to the parentmodel 310. The dependency tree 305 is sequentially constructed based ona series of queries spawned from the retrieval of model identifiers thatare dependent on the parent model. For example, a query on modelidentifier “10400” returns two dependent models; “10384” and “10196”.MVU 145 then issues queries to retrieve model identifiers for modelsthat are dependent on models “10384” and “10196.” This process maycontinue until no further model dependencies are found. From this data,the model dependency tree is constructed through any number of knownmethods for constructing a data tree.

In addition to the model identifier, information further defining themodels in the dependency tree 305 may be displayed providing user 105with a quick overview of the general attributes of the models. Forexample, the models may be displayed along with a business unitidentifier, creation date, frequency of use, record count, and/or thelike. Each model in the tree structure further includes a hyperlinkthat, when selected, invokes MVU 145 to issue a query against the Modeltable 230 to retrieve metadata information. The metadata information isformatted and presented in tabular form on a portion of the web page asa mini-metadata report 315. Information included in the mini-metadatareport may include, for example, model label, model identifier, name ofthe modeler, email address for the modeler, model owner, number ofrecords associated with the model, frequency of use, business group,population segment, model program environment, population, and mostrecent deployment date. The model identifier in the mini-metadata reportis hyperlinked to a detailed metadata report web page, which will bediscussed in greater detail in reference to FIG. 4. In anotherembodiment, model metadata for both parent and child models are returnedwith the tree structure and are maintained within the hypertext languagedocument, thereby eliminating the need for MVU 145 to issue a new queryevery time user 105 clicks on a different model in the tree structure.

Model Dependency web page 300 further includes navigation and viewcontrols to enable user 105 to zoom in or out of the dependency tree305. This may be desirable as model dependencies can become very largeand complex. A search utility may further provide user 105 the abilityto quickly locate specific models within a complex dependency tree 305.Preferences may be defined by user 105 to control the appearance andbehavior of the Model Dependency web page 300. Such preferences mayinclude, for example, display colors, information to display withinmodels, navigation, sizing, zooming, and/or the like.

In one embodiment, each model displayed within the tree structureincludes an expand/retract hyperlink enabling user 105 to control whichlevels of the dependencies to view. For example, if a parent model hastwo child models and six grandchild models branching from the childmodels, user 105 may select a link on one or both of the child models tohide or show the grandchild models.

In another embodiment, the models in the tree structure are color coded.Varying colors are used to indicate that a model has been marked fordeletion, has not been used for a defined period of time, is a newmodel, and/or the like. Furthermore, models may be color coded accordingto model label, model identifier, modeler, model owner, number ofrecords associated with the model, frequency of use, business group,population segment, model program environment, population, and mostrecent deployment date.

To view detailed metadata information relating to a selected model, user105 may select the hyperlinked model identifier field in the minimetadata report 315. A metadata report web page may also be accessedfrom the Model Insight home page. As previously described, both modeldependency and model metadata options may comprise dropdown menus thatare pre-populated with a list of model identifiers that are retrievedfrom the Model table 230. After selecting a model identifier in themodel metadata dropdown, user 105 is presented with a Metadata Reportweb page.

In addition to the various view controls discussed above in reference toFIG. 3, the invention contemplates the incorporation of othercapabilities to expand the user's ability to manage a complex modelingenvironment in view of the perspectives gained by the various insightand reporting tools disclosed herein. A brief description of thesevarious capabilities is hereby disclosed. Practitioners will appreciatethat for the sake of brevity, the incorporation of the herein disclosedcapabilities will not be repeated with each of the described interfacespresented in FIGS. 3 through 16.

The disclosed interfaces may incorporate color coding in a mannersimilar to that which is disclosed above in reference to FIG. 3. Colorcoding or any other visual modification of interface elements may behelpful in helping user 105 to more quickly and accurately discernvariations in values, properties, status, and the like. Moreover, visualvariations may be incorporated to draw the attention of user 105 tocritical elements where closer analysis may be helpful or essential. Forexample, if a model appearing in the dependency tree described above isslated for decommissioning, it may be beneficial to highlight thecorresponding model node to enable user 105 to quickly determine whetherthe decommissioning status will have an effect on decisioning efforts.

Display preferences, also discussed above, may include any configurableproperties relating to the manner in which various interface elementsare displayed. As will be described in greater detail herein, suchdisplay preferences may define whether data is presented in tabular formor within a graph. Display preferences may further enable a user toconfigure a color scheme for the visual variations described above. Forexample, user 105 may configure MVS 175 to display all models that havenot been used over the past ninety days in yellow. Display preferencesmay further include magnification, scrolling, report deliverypreferences, audio alerts, visual alerts, and the like.

When appropriate, the herein disclosed interfaces may include variousconfigurations of search tools. Such search tools may be provided withthe bounds of the interface, on a separate web page, within a dialogbox, as a popup, and the like. Search tools enable user 105 to entercriteria specific to the information that the user 105 is mostinterested in reviewing. In some cases, this may include very specificcriteria such as a model name, for instance. Under other scenarios, thesearch criteria may be less rigid such as, for example, to retrieve allvariables developed by an identified model developer. Practitioners willappreciate that there are a number of ways in which data can be searchedwithin a database including via multiple parameters, Boolean, wildcard,and/or the like.

A number of reports and reporting interfaces are disclosed herein.Reports may further be ordered for delivery to a printer, or to anynumber of email recipients. The various interfaces may enable user 105to designate a reporting group (i.e., who is to receive a report), adelivery mechanism (e.g., email, facsimile, printer, PDA device,cellular telephone), and define a reoccurring reporting schedule (e.g.,annual, monthly, weekly, daily). For example, user 105 may select a linkon the Model Analysis Detailed Report interface (FIG. 7) to setup areoccurring report. An interface is provided whereby user 105 may selectan option to have MVS 175 run the report on the last day of each month,select from a list of authorized users to whom the report is to bedelivered, and select the delivery mechanism.

In one embodiment, an email may be generated for delivery to any numberof individuals or mailing groups. The email message may contain a linkwherein, selection of the link automatically opens a browserapplication, retrieves a saved report, and provides the report withinthe appropriate interface. In another embodiment, the report may beincluded within the body of the email message itself.

Notation tools provide for increased cohesion between individualsworking independently within a modeling environment. For example, amodel metadata report may enable user 105 to enter a notation and attachit to a specific variable or metadata element in order to subsequentlyalert other users that models relying on the specific variable haveproduced erroneous data. Moreover, entry of a notation may invoke anautomatic generation of an email message that will be delivered to anynumber of identified users. Such identified users may include, forexample, managers, model developers, marketing personnel, and the like.

While the various interfaces are described herein in terms of analysisand reporting tools, the invention further contemplates that user 105may interact with any of the interfaces to enter, modify, and/or deletedata relating to metadata, variables, and models. Various levels ofediting may be permitted according to user privileges that have beendefined and stored at user database 130. For example, only anadministrator may be permitted to delete a variable, but a developer maybe permitted to modify metadata. Such modifications may further besubject to authorization by any one or more defined users. For example,an administrator may select a variable in a model metadata report todelete, however, the deletion will not occur until the model owner anddeveloper have been notified and authorize the deletion. According tothis embodiment, the invention further contemplates a workflow managerto ensure adherence to organizational policies and to safeguard amodeling environment against the erroneous modifications of any singleuser.

Similar to the editing abilities described above, the system may alsoprovide decommissioning tools. When it is determined that a model orvariable have become obsolete, are no longer used, or provide inaccurateoutput, it may be desirable to remove it from the modeling environment.However, as will be discussed in greater detail herein, removing a modelmay have far reaching consequences due to interdependencies among modelsand model variables. Thus, when analysis proves that a model should bedecommissioned, the system may control the processes, such that theappropriate personnel are notified and that appropriate authorizationsare obtained. MVS 175 further incorporates intelligence tools thatprevent the removal of models and variables when it is determined thatsuch removal will compromise the integrity of the modeling environment.MVS 175 may only permit the removal when issues of dependencies areresolved or on authorization from a super user.

FIG. 4 is a screenshot of an exemplary web page for displaying adetailed report of metadata related to model variables. The reportincludes a list of variables such that when the user selects (e.g.,mouse over event, clicks, etc) a variable, current score values relevantto that variable are displayed. As the Metadata Report web page 400 isloading at web client 110, MVU 145 executes a query against theDataset_Col table 235 with a join request to the Model table 230 toretrieve a list of variables associated with the selected model.Variables are listed by name in tabular form (in Variable Name chart405) following a header portion displaying the model identifier 410 andreport date 415. A field displaying the total number of variables mayalso be displayed within the Metadata Report web page 400.

User 105 may view complete details for any of the listed variables byplacing a mouse cursor over a variable name. MVU 145 obtains variabledetails by first issuing a query against the Variables table 210 todetermine the variable type. In one embodiment, variables arecategorized among two types; “Continuous” or “Categorical.”

Continuous variables are infinite and statistically defined as intervaland ratio numerical values. Statistical values (univariate data) forcontinuous variables may include, for example, min, max, mean, missing,count, range, sum, p1, p5, p10, etc. In one embodiment, MVS 175organizes and displays continuous variables within a histogram.Categorical variables are statistically defined as nominal and ordinalvalues. Categorical variables are alpha-numeric values that, accordingto one embodiment, are organized and displayed as a pie chart.Statistics calculated for categorical variables are top 5 attributes inpercentage and absolute terms.

From the Variables table 210, MVU 145 determines the value of the“var_type_cd” field. If the value is equal to “CH” (Char) or “DT”(Date), then the variable is “categorical.” If the “var_type_cd” fieldvalue is equal to “EN” (Numeric) or “MD” (Model's score variable), thenthe variable is “continuous.”

The variable type determines which table to query to retrieve variabledetails. If the variable is determined to be of type “Categorical”, thenMVU 145 queries the Category_Variables table 205, otherwise the query isdirected to the Continue_Variables table 200. The variable details areretrieved based on the most recent report generation date as recorded in“Rpt_Gen_Date” fields in either the Category_Variables table 205 or theContinue_Variables table 200.

To aid in the analysis of a model and its distribution in light ofvarious conditions, user 105 may select a link from the home page toview a Model Analysis web page. Referring to FIG. 5, the Model Analysisweb page 500 includes a number of model selection parameters presentedin dropdown menus 505. Such model selection parameters may include, forexample, variable identifier 510, frequency of use, business group,model type, target system, modeler identifier, table name, and/or thelike. Thus, if user 105 would like to perform an analysis on a specificgroup of models, she may select any number of criteria to narrow theretrieval to those models that she is most interested in viewing.Moreover, user 105 may retrieve models according to text entry of avariable name or partial variable name entered in a text search field510.

User 105 may select a report type 515 and the primary axis 520 fromcorresponding dropdown menus. Practitioners will appreciate that system100 may incorporate any number of presentations to represent modeldistribution and model use within a report. For example, user 105 mayselect to view a selection of models in a pie chart, which provides asimple representation of model distribution in a comparative manner.User 105 may further choose to view reports at varying levels of detailincluding, for example, a summary report and a detailed report. Theselection of the primary axis 520 defines the presentation of a graph,in that the retrieved models will be segregated according to the primaryaxis selection.

For example, if a variable relates to the interest rate for a financialproduct, a user may increase this variable then determine how such anincrease will affect the various models that are impacted by thevariable. More specifically, if the interest rate is raised from 5% to10%, then the user may see that a model which models financial productpurchases in the southeastern United States shows a decrease in thenumber of expected financial product purchases to decrease due toconsumers historically not desiring a financial product with such a highrate.

To submit the selected parameters, report type, and primary axis to MVS175, user 105 selects an “Execute” link 525. The selected parameters,report type, and primary axis are submitted and processed by MVS 175 ina manner similar to that described above in reference to FIG. 3. Theselected parameters are used to query model database 150 and retrievedata corresponding to the selection parameters. The retrieved model datais formatted according to the report type 515 and primary axis 520selections within a hypertext language document and is transmitted toweb client 110 for review by user 105.

FIG. 6 is a model summary report that includes a report date 605 andvariable name 610. Displayed in tabular form, the report lists all (orany subset of) models which are directly impacted (in chart 615) by theselected variable identified at 610. User 105 may further view thenumber of models directly affected by the selected model 620. A secondtable displays models that are indirectly impacted by the selectedvariable as identified at 610. In other words, table 625 lists modelsthat are directly impacted by the selected variable. Informationregarding model dependencies is retrieved by MVU 145 based on a queryexecuted against the Model_Dependency table. While not illustrated,Summary Report 600 web page includes a sum of models that are indirectlyimpacted by the variable identified at 610. Also not shown, the SummaryReport web page 600 includes a display of the search parameters whichformed the basis of the present report.

The model identifiers for both directly and indirectly impacted modelsinclude hyperlinks which link to the Model Dependency web page describedin reference to FIG. 3. The selection of any of the displayed modelidentifiers invokes MVU 145 to construct a tree structure representativeof the variable dependencies in the manner described above. Selection ofa model identifier from the Summary Report web page 600 causes the ModelDependency web page to open in a new window, thus preserving the contentof Summary Report web page 600 while allowing user 105 to drill in andview one or more model dependencies in the tree structure.

FIG. 7 is a screenshot of an exemplary interface for displaying detailedmodel attributes in accordance with an embodiment of the presentinvention. For a more holistic view of model details, user 105 mayselect to view a Detailed Report web page 700 that presents models withsufficient detail to enable user 105 to quickly discern modelattributes. In one embodiment, only models that are directly impacted bythe selected variable are displayed within the Detailed Report web page700. The total number of directly impacted modules may also be shown at720. However, practitioners will appreciate that a detailed display ofindirectly impacted models may provide additional benefits. When theDetailed Report web page 700 is constructed, the report date 705 isrecorded and the variable name 710 from which the displayed modelsdepend is listed. Model details are displayed in tabular form 715 andinclude columns to display, for example, model identifier, modeldescription, type, modeler identifier, business unit, frequency,population selection, target, record count, latest deployment date,and/or any other information that can be captured from the Model table230 or related table. While not shown, the Detailed Report web page 700includes a display of the search parameters which formed the basis ofthe present report.

The model identifiers for the listed models 715 include hyperlinks whichlink to the Model Dependency web page described in reference to FIG. 3.The selection of any of the displayed model identifiers invokes MVU 145to construct a tree structure representative of the variabledependencies in the manner described above. Selection of a modelidentifier from the Detailed Report web page 700 may cause the ModelDependency web page to open in a new window, thus preserving the contentof the Detailed Report web page 700, while allowing user 105 to drill inand view one or more models dependencies in the tree structure.

With reference to FIG. 8, a Chart Reports web page 800 provides user 105with a graphical representation 805 of model distribution based on theprimary axis 520 that user 105 selected at the Model Analysis web page.As the Chart Report web page 800 is loaded, MVU 145 executes a queryagainst the Model table 230 according to the search criteria selected atthe Model Analysis web page. Results are grouped according to theselected primary axis and formatted into a graph 810 to be incorporatedwithin the Chart Reports web page 800. Practitioners will appreciatethat there are a number of commercially available charting utilitiesthat accept data in addition to parameters to construct a graphicalrepresentation of the data for incorporation within a web page.Moreover, report engine 155 may be invoked by application server 135 toconstruct a graph of varying types. One such report engine is CrystalReports® by BusinessObjects™. Crystal Reports enables developers toquickly configure and incorporate sophisticated reports and graphswithin custom computer applications and web pages.

System 100 provides various other reporting capabilities in addition tothe analysis of models as described above. Specifically, system 100enables user 105 to view reports reflecting variable usage by businessunit, variable usage by model type, variable metadata, and systemperformance. Such reports provide user 105 with greater insight into theactual usage of models and model variables and system performance as itrelates to modeling procedures, as opposed to the analysis of modeldistribution and dependencies, as described above. Practitioners willappreciate that the report types disclosed herein may represent only asampling of report types that may be provided to users of system 100.

From the Model Vision home page, user 105 may select a link to view theModel Reports home page. The Model Reports home page further provideslinks to other web pages providing specific reporting capabilities,which are described in greater detail herein. Practitioners willappreciate that the links may be presented in the form of hyperlinks,dropdown menus, check boxes, and/or the like.

Referring to FIG. 9, after selecting a link to view variable usage bybusiness unit, MVU 145 executes a query against Model table 230 toretrieve and categorize variable data according to business unit. Thecategorized variable data is displayed in tabular form within theVariable Usage by Business Unit web page 900 and includes a first table910 displaying variable usage data by percentage and a second table 920displaying variable usage data by absolute numbers.

Each of first table 910 and second table 920 include a row for eachbusiness unit. Each business unit row includes a Model Type dropdownmenu 915 populated with distinct values available from the Mdl_Type_CDfield of the Model table 230. Each of first table 910 and second table920 further include columns representing the model count per businessunit and a series of variable classification columns to display thedistribution of the variables among each classification. When a value isselected from a Model Type dropdown menu 915, the number of modelsdependent on the combination of variables is updated in the Model Countcolumn. Further, the percentage/absolute number distribution of thevariables corresponding to the combination of business unit and selectedModel Type 915 are displayed within that row.

The column representing variable classification in first table 910 issubdivided. A first subdivision represents a percentage of models thatuses at least one variable of that variable type. A second subdivisionrepresents a percentile value indicating the percentage of variables ofthat type, which are used in at least one model.

The Variable Usage by Business Unit web page 900 further includes a dateselection dropdown menu 930 to enable user 105 to select previouslygenerated reports. When a variable usage report is executed, MVS 175 mayautomatically save the report to an archive table within model database150. In another embodiment, MVS 175 may be configured to run the reportat regular intervals (i.e., monthly). Thus, when user 105 desires toview a previous report, she may select a report date from the dateselection dropdown menu 930, invoking MVU 145 to execute a query againstan archive table to retrieve data representative of the state ofvariable usage for the selected report date. This may be useful, forexample, in determining how changes to models or variables have affectedvariable usage over time.

To view a report containing the sum of variables under each variableclassification, user 105 may select a link 935 to launch the report in anew window. Referring to FIG. 10, the Variable Count Report web page1000 includes a report date 1005 and a table comprising a column forvariable classification and a column displaying the number of variables.Each row of the table represents a variable classification and acorresponding number of variables presently falling within the variableclassification. User 105 may further view previous reports by selectinga report date from a date selection dropdown menu 1015. When user 105selects a “View Report” link 1020, MVU 145 executes a query against anarchive table to retrieve data representative of the state of variablecounts for the selected report date. This information may be obtainedfrom the archived Variable Usage by Business Unit records.

Similar to the Variable Usage by Business Unit web page 900 describedabove, system 100 may provide a Variable Usage by Model Type web page,wherein the report is based on the model type within the variableBusiness Unit. While such report is not shown, the values displayed inthe Variable Classification columns are determined, in one embodiment,according to the combination of model type and the value selected in theBusiness Unit dropdown menu. The Variable Usage by Model Type reportalso includes a link to view variable count report containing the sum ofvariables under each variable classification.

With reference to FIG. 11, when user 105 selects a link to view avariable metadata report, MVU 145 executes a query against modeldatabase 150 to retrieve values to populate a date dropdown menu,variable classification dropdown menu, and variable/model dropdown menu.After retrieving the values and populating the associated dropdown menu,user 105 is presented with the Variable Metadata Report web page 1100.This report enables user 105 to view metadata according to selectioncriteria that includes date 1105, variable classification 1110, andvariable/model 1115. Selection of a “View Report” link 1120 invokes MVU145 to execute a query against the Variables table 210 to determine thevariable type. If the variable type belongs to the “Continuous” variabletype, then MVU 145 issues a query against the Continue_Variables table200. If the variable type belongs to the “Categorical” type, then MVU145 issues a query against the Category_Variables table 205.

Values returned by MVU 145 are displayed in tabular form 1125 andincludes columns for Attribute, Distribution Percentage, and Count. Suchinformation provides valuable insight into the model and variableenvironments and for facilitating the institutionalization of customerbehavior into corporate memory for strategic analysis. Analysis ofvariable metadata may provide insight for proactive identification ofdata anomalies, facilitation of strategy development and execution,population profiling prior to model development, and populationprofiling to determine population sizing, targeting, and segmentation.

Complex modeling environments can strain system performance, and thevarious interfaces and reports described above may be used to identifyproblematic models and variables. However, monitoring system performanceat regular intervals will give an administrator advance notice as topotential problems or system degradation. In order to provide aproactive means for monitoring system performance, in one embodiment,system 100 includes a system performance report.

User 105 may select a link to access the System Performance Report webpage 1200. The System Performance Report web page 1200 includes a monthselection dropdown menu 1205 that is pre-loaded with the previous twelvemonths. Practitioners will appreciate that the month selection dropdownmenu 1205 may be preloaded with any number of months depending on therecord retention policy of the administering entity. Furthermore,practitioners will appreciate that a system performance report may begenerated in accordance with any selected time interval. For example,the month selection dropdown menu 1205 may allow user 105 to select aday, week, or year. In one embodiment, the interface includes dateselection dropdown menus; a first representing a start date, and asecond representing an end date. Performance data would then beretrieved when it falls between the two selected dates.

In one embodiment, the user may select parameters to view a morespecific subset of performance data. For example, user 105 may select adeveloper identifier, business unit, model type, or variable type inorder to retrieve performance data related to the selection.

User 105 may select a month for which to view a system performancereport and further select whether she would like to view the report intabular 1210 or chart 1215 form. After selecting an “Execute” link 1220,MVU 145 executes a query against the Model_Sum table 255 to retrieve allrecords that fall within the selected month 1205. The retrievedperformance data is transmitted to web client 110 where it is displayedwithin a table 1225. The table includes columns for displaying theweekday, date, number of models scored, time to score all models,average dataset generation time, average dataset transfer time, averagemodel score time, and average model transfer time.

Monitoring system performance is vital in any organization whereday-to-day operations rely on a network of computing systems anddatabases. In a complex modeling environment, modeling systems canbecome over-taxed, in time, leading to reduced system performance andadditional expenditures directed toward the expansion of the computinginfrastructure. As the business environment changes, some models andvariables may become obsolete. Over time, obsolete or unused models cancomplicate the modeling environment and unnecessarily consume systemresources. Moreover, as a modeling system grows with vast populations ofmodels, managing the models becomes very difficult and time consuming.Without total insight into the modeling system, developers may continueto create models that already exist, thereby duplicating developmentefforts and taxing human resources. MCS 175 provides insight into modelusage and human resource allocation in order to help administrators,project managers, and developers to maintain an efficient model andmodel development environment.

Providing a view into overall variable and model usage enablesadministrators to optimize system performance and technology investment,accelerate model development and execution, and accommodate a growingdemand for new data and models.

Referring to FIG. 13, user 105 may also view a system performance reportin chart form by selecting a report month from the month selectiondropdown 1305 on webpage 1300, selecting the “Chart” option 1310, andselecting a value from the Y-axis dropdown menu 1315. As the daysfalling within the selected month 1305 will be used for the X-axis, theY-axis is selected by user 105 and may include, for example, number ofmodels scored, time to score all models, average dataset generationtime, and/or average model execution time. After selecting an “Execute”link 1320, MVU 145 executes a query against the Model _Sum table 255 toretrieve all records that fall within the selected month 1305. Theretrieved performance data is formatted into a chart and is transmittedto web client 110 where it is displayed 1325.

The ability to manage human resource allocation and performance is acritical function in a modeling development environment. Insight intohow human resources are being allocated provides for succession planningfor business continuity, reward and recognition, and the like. Referringto FIG. 14, when user 105 selects a link to view a Human ResourcesAllocation interface 1400, MVU 145 executes a query against the Modeltable 230 with a join to the Model_Owner table 245 to retrieve the fullname for each developer of models in the Models table 230. The retrievedmodel owner data is formatted into a graph and is transmitted to webclient 110 for viewing by user 105.

The Human Resources Allocation chart 1400 includes a horizontal axis(X-axis) 1410 representing each model owner name and a vertical axis(Y-axis) 1405 representing the number of models developed by each modelowner. The Human Resource Allocation graph 1400 provides user 105 withan overview of how work among a number of developers is being allocated.For example, a project manager may view the graph to determine if any ofher employees are being assigned workloads that are either to heavy orto light. With such information, the project manager may shift tasksamong developers to increase the efficiency and/or productivity of theworkforce. According to the example shown in FIG. 14, a project managermay assign fewer model development tasks to “Employee1” 1415 and assignmore development tasks to the developers with fewer models to maintain.Practitioners will appreciate that human resource allocation informationmay be displayed in any number of formats, including tables, charts, andgraphs.

In one embodiment, user 105 is presented with an interface to define theX-axis. For example, rather than view the Human Resource Allocationgraph 1400 according to the number of models deployed, user 105 mayprefer to view the graph in terms of the time to deployment, by modeltype, by business unit, and the like. Configuring the x-axis enablesuser 105 to drill in and view even more specific information that may behelpful in the task of human resource allocation.

With reference to FIG. 15, a Model Usage Report interface 1500 includesthe display of the breakdown of models in terms of their frequency ofuse. As the Model Usage Report web page 1500 is loaded, MVU 145 executesa query against the model table 230 to retrieve model counts and usagedata. In one embodiment, user 105 is presented with a model selectioninterface (not shown), wherein model subsets may be selected from thepool of stored models. The models selection interface may includepre-populated dropdown menus, for example, to enable user 105 to defineselection parameters such as, for example, model type and usagetimeframe.

The model usage report is displayed in tabular form that includes avertical column 1520 which represents varying frequency codes from modeltable 230 such as, for example, “On Demand”, “Daily”, “Weekly”, and“Monthly.” Frequency codes classify models according to their intendedusage. For example, a model designed to simulate card member spend basedon a marketing campaign may utilize data from monthly spend, thus themodel may be categorized as “Monthly.”

MVU 145 also categorizes models according to predefined use intervals,indicative of when a model was most recently used. When a model is used,a date/timestamp field is updated in the Models table 230; therefore,each model is tested to determine if it has been used in the last month1505, in the last two to three months 1510, or in the past four to fivemonths 1515. Practitioners will appreciate that models may becategorized according to any number of frequency codes and/or useintervals. Viewing the Model Usage Report interface 1500, anadministrator can quickly identify models to decommission in order tolift the burden of system resources.

In one embodiment, counts appearing in the cells of the Model UsageReport may include a hyperlink to view more specifics regarding theidentity and characteristics of the models within the selected category.For example, an administrator may select the “On Demand” models thathave not been used for “4-5 Months.” With reference to FIG. 7, aDetailed Report web page 700 is constructed, wherein model details aredisplayed in tabular form 715 and include columns to display, forexample, model identifier, model description, type, modeler identifier,business unit, frequency, population selection, target, record count,latest deployment date, and/or any other information that can becaptured from the Model table 230 or related table.

To provide insight into marketing campaign penetration and decisionsciences, MVS 175 provides an interface to view graphs representative ofthe penetration of models in campaigns, the types of decision sciencesused in customer marketing, and an overview of the number of models usedin various campaigns. FIG. 16 is a screenshot showing two graphs thatenable user 105 to analyze specific model usage in regard to marketingcampaigns types. When user 105 selects a link to the penetration anddecision sciences web page 1600, MVU 145 executes a query against themodels table in model database 150 to retrieve data indicative of thetypes of campaigns which incorporate each model. MVU 145 categorizesthis data and constructs a chart according to the user's 105preferences, before constructing a web page to be displayed at webclient 110. Practitioners will appreciate that an interface may beprovided, whereby the user can select formatting, graphing, and/orcharting preferences.

A “penetration of models in campaigns” graph 1605 includes a horizontalaxis (X-axis) 1615 representing various categories of marketingcampaigns and a vertical axis (Y-axis) 1610 representing the percentageof models used in each of the campaign categories. In the examplepresented at 1605, the overall percentage of models used for campaignsdirected toward encouraging credit card holders to increase theirspending is less than 5%. The percentage of models used in servicerelated campaigns is approximately 22%. A raw number of models usedamong each classification of campaigns may further be displayed. Thus,the penetration of models in campaigns graph 1605 provides aneasy-to-decipher, overall view of how models within a modelingenvironment are being used. For example, the graph 1605 may prompt anadministrator to investigate why so few models are being used in spendrelated marketing campaigns.

In one embodiment, the various graph segments include a hyperlink thatlinks to the Model Analysis Detailed Report (FIG. 7) where user 105 mayview details relating to each of the models in the selected marketingcampaign category. As such, user 105 may perform detailed analysis tofurther determine, for example, why there are so few/many models used inthe selected marketing campaign category.

A “Types of Decision Sciences Used in Customer Marketing” graph 1620includes a horizontal axis (X-axis) 1630 representing various categoriesof marketing campaigns and a vertical axis (Y-axis) 1625 representingthe number of models used in each of the campaign categories. The X-axisis further subdivided to provide a more specific view into how modelsare being used in relation to specific decision sciences. In the exampleprovided at 1620, user 105 can determine that for models used forcampaigns directed toward encouraging credit card holders to increasetheir spending, a very small number (e.g., approximately five) of modelsare used for the “risk” decision science. Again, graph 1620 may promptan administrator to investigate why so few models are being used in riskrelated decision sciences.

In one embodiment, the various graph segments include a hyperlink thatlinks to the Model Analysis Detailed Report (FIG. 7), where user 105 mayview details relating to each of the models in the selected decisionsciences category. As such, user 105 may perform detailed analysis tofurther determine, for example, why there are so few/many models used inthe selected decision sciences category.

While the screenshots and steps outlined above represent a specificembodiment of the invention, practitioners will appreciate that thereare any number of computing algorithms and user interfaces that may beapplied to create similar results. The steps are presented for the sakeof explanation only and are not intended to limit the scope of theinvention in any way.

Benefits, other advantages, and solutions to problems have beendescribed herein with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any element(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as critical, required, or essentialfeatures or elements of any or all the claims or the invention. Itshould be understood that the detailed description and specificexamples, indicating exemplary embodiments of the invention, are givenfor purposes of illustration only and not as limitations. Many changesand modifications within the scope of the instant invention may be madewithout departing from the spirit thereof, and the invention includesall such modifications. Corresponding structures, materials, acts, andequivalents of all elements in the claims below are intended to includeany structure, material, or acts for performing the functions incombination with other claim elements as specifically claimed. The scopeof the invention should be determined by the appended claims and theirlegal equivalents, rather than by the examples given above.

As used herein, an element in the singular is not intended to mean “oneand only one” unless explicitly so stated, but rather “one or more.”Moreover, where a phrase similar to ‘at least one of A, B, and C’ isused in the claims, it is intended that the phrase be interpreted tomean that A alone may be present in an embodiment, B alone may bepresent in an embodiment, C alone may be present in an embodiment, orthat any combination of the elements A, B and C may be present in asingle embodiment; for example, A and B, A and C, B and C, or A and Band C.

1. A method for creating a visualization of a plurality of simulationmodels, said method including: analyzing, by a computer for creatingsaid visualization of said plurality of simulation models, dependencydata from said plurality of simulation models, each of said plurality ofsimulation models having a model identifier; determining, firstvariables within a first subset of said plurality of simulation models,wherein said first subset of said plurality of simulation models isimpacted by said dependency data, wherein said plurality of simulationmodels simulate outcomes, effectiveness, penetration, utilization, anddistribution of marketing strategies based upon at least one ofhistoric, current or probability data of said marketing strategies, andwherein said dependency data depends upon and includes records having anidentifier that is based upon said model identifier and that depend atleast one of directly or indirectly from a simulation model of saidplurality of simulation models; analyzing, by said computer, saiddependency data relating to said first subset of said plurality ofsimulation models; determining second variables within a second subsetof said plurality of simulation models, wherein said second subset ofsaid plurality of simulation models is impacted by said dependency data,wherein said second subset of said plurality of simulation models isdependent upon said first subset of said plurality of simulation models,and wherein said dependency data relates to a transfer of informationexchanged between at least one of said first variables or said secondvariables, and at least one of said first subset of said plurality ofsimulation models and said second subset of said plurality of simulationmodels, wherein said information includes accuracy of said information,an amount of said information, a transfer rate of said information, aprocessing rate of said information, and usage data for each of variabletypes; determining, by said computer and based upon said modelidentifier, inter-dependencies of model identifiers across saidplurality of simulation models; propagating, by said computer, and basedon said model identifier and said inter dependencies, a change to aselect variable to at least one of said first subset of said pluralityof simulation models or said second subset of said plurality ofsimulation models; reading, by said computer, model data that includeslast usage date for each of said plurality of simulation models;categorizing, by said computer, each of said plurality of simulationmodels according to said last usage date and variable usage type; andcreating, by said computer, said visualization of said plurality ofsimulation models according to said categorization, wherein saidvisualization shows dependencies of said plurality of simulation modelsbased upon said dependency data, wherein said dependencies are basedupon how said plurality of simulation models utilize said first subsetof said plurality of simulation models and said second subset of saidplurality of simulation models.
 2. The method of claim 1, furtherincluding categorizing each of said plurality of simulation modelsaccording to a usage frequency indicator.
 3. The method of claim 2,wherein usage frequency indicator includes at least one of on demand,daily, weekly, monthly, quarterly, semi-annually, and annually.
 4. Themethod of claim 1, wherein said visualization includes at least one of atable, graph, and chart.
 5. The method of claim 1, wherein said modeldata includes last usage date for a type of said plurality of simulationmodels.
 6. The method of claim 1, wherein elements of said visualizationare color-coded according to at least one of: status, modeler, modelowner, business unit, model type, variable type, decommissioning status,decommissioning date, and deployment date.
 7. The method of claim 1,further including receiving a request to decommission a model associatedwith said model data, wherein said request includes at least one of:decommission date, authorization request, model dependency data,variable dependency data, and model owner identifier.
 8. The method ofclaim 7 wherein said model is decommissioned only in response todependencies being resolved.
 9. The method of claim 1, further includingreading a request to decommission one of at least one of said firstvariables or said second variables associated with said model data,wherein said request includes at least one of decommission date,authorization request, model dependency data, variable dependency data,and model owner identifier.
 10. The method of claim 9, wherein said oneof at least one of said first variables or said second variables isdecommissioned only in response to dependencies being resolved.
 11. Themethod of claim 1, wherein said model data is received from a pluralityof modeling environments.
 12. The method of claim 1, wherein saidvisualization is distributed by at least one of markup language file,email, facsimile, wireless device, and postal mail.
 13. The method ofclaim 1, further including reading dependency data from each of saidplurality of simulation models associated with said model data, whereinsaid dependencies are based upon how a simulation model utilizes otherof said plurality of simulation models.
 14. The method of claim 1,further including creating a visualization showing a plurality ofvariable names, wherein each of a plurality of variable valuescorresponding to each of said plurality of variable names is displayedin response to a processor event.
 15. The method of claim 1, furtherincluding: retrieving, from said model data, dependency data in responseto said model data including a variable name; and categorizing saidmodel data according to said dependency data, wherein said plurality ofsimulation models are arranged according to said dependency data. 16.The method of claim 1, further including: reading variable datacorresponding to said model data, wherein said variable data includesusage data for said at least one of said first variables or said secondvariables; categorizing said at least one of said first variables orsaid second variables according to said usage data to create variabletypes; and determining usage statistics fir each of said variable types.17. The method of claim 1, further including: reading performance datacorresponding to said model data, wherein said performance data includesmodel scoring data, model execution data, and dataset generation datafor a plurality of simulation models; and determining averages for saidperformance data based on defined time intervals.
 18. The method ofclaim 1, further including: retrieving, from said model data,identifiers for a plurality of model developers; and categorizing saididentifiers according to an allocation type identifier, wherein saidcategorization includes allocation of human resources based upon saidallocation type identifier.
 19. The method of claim 1, furtherincluding: retrieving, from said model data, a simulation type indicatorfor each of said plurality of simulation models; and categorizing, saidplurality of simulation models according to said simulation typeindicator, wherein said categorization includes a count indicatorcorresponding to each of said simulation type indicator.
 20. The methodof claim 1, further including: retrieving, from said model data, a firstsimulation type indicator and a second simulation type indicator foreach of said plurality of simulation models; and categorizing saidplurality of simulation models according to said first simulation typeindicator and said second simulation type indicator.
 21. The method ofclaim 1, further including receiving, by said computer, one or moreselection parameters, wherein the one or more selection parameters areused to query a model database and retrieve model data.